Most fire detection systems generate an alarm condition in response to a measured environmental factor that indicates the existence of a fire. Photoelectric smoke detectors, for example, determine a light obscuration level in sampled air and trigger an alarm condition when this obscuration exceeds some predetermined threshold. In most cases, the obscuration is due to smoke in the atmosphere. Many thermal fire detectors operate on a similar principle. They will trigger the alarm when the measured ambient temperature reaches 130.degree. F., for example.
One improvement to these threshold-based detector systems is the maintenance of a running average or quiescent value against which each current sample is compared. For example, in the smoke detectors, a long-term running average, over 24 hours for example, is kept for the detected obscuration levels, and the current sample is compared against this average. An alarm condition is generated when a current sample exceeds this average obscuration by the threshold, which does not change in time. The advantage of this approach is that the smoke detectors will maintain substantially the same sensitivity over time, mitigating the effects of aging and dirt accumulation in the detection chamber.
A similar approach is taken with the heat detectors. The time period over which the running average is kept, however, tends to be shorter to account for the fact that the temperatures within buildings change across a 24 hour period. Thus, the smoke detector will have substantially the same sensitivity at night, when the building is cold, and during the day when the building tends to be hotter.
In order to improve early detection capabilities, various systems have been proposed that generate alarms based not upon the net level of the sampled physical phenomenon but on the changes or trends in the sampled data. One of the earliest examples of this type of system is disclosed in U.S. Pat. No. 4,254,414 to Street, et al. The disclosed processor-aided fire detector tracks the sample-to-sample changes in the detected obscuration levels. The detector generates various levels of alarms based upon the time over which the atmospheric obscuration has been continuously increasing. Rate-of-rise temperature detectors rely on a similar approach. These devices generate an alarm when the temperature is increasing quickly over a defined period of time. The assumption is that this rapid temperature rise is, with high probability, initiated by fire.
In general, these trend-based devices tend to have good early detection characteristics, but can be subject to higher instances of false alarms. It is problematic to filter the data to ensure that random events occurring over the course of years do not satisfy the trend criteria necessary to activate the alarm.
In order to improve the fire detector's resistance to false alarms and improve uniformity over a wide range of fire types, a number of different approaches have focused on generating alarms based upon the outputs of two or more sensors. Researchers have studied the cross correlations between the changes in temperature; smoke density according to extinction effects or scattering effects; effects on ion flow in a measuring ionization chamber; and concentrations of carbon monoxide, carbon dioxide, total hydrocarbons, and oxides of nitrogen as predictors of fire. See Fire Detection Using Signal Cross Correlation Techniques, by G. Heskestad, et al., Factory Mutual Research Corporation.